Data centers are consuming ever more energy. Recognizing that cooling is a significant contributor to energy consumption, data center operators are beginning to tolerate higher operating temperatures. While this practice saves substantial amounts of energy, running closer to allowable operating temperature limits increases the risk that temperature problems will result in equipment failures that wipe out the financial benefits of saving energy. Vigilance is needed, and increasingly that vigilance is being provided by data center energy management software that monitors data center environmental conditions, such as temperature, and alerts operators when troublesome hot spots develop.
A number of techniques have been proposed or suggested for employing one or more robots to automatically navigate, map and monitor data centers. For example, J. Lenchner et al., “Towards Data Center Self-Diagnosis Using a Mobile Robot,” ACM Int'l Conf on Autonomic Computing (ICAC '11) (2011), incorporated by reference herein, discloses a robot that serves as a physical autonomic element to automatically navigate, map and monitor data centers. The disclosed robot navigates a data center, mapping its layout and monitoring its temperature and other quantities of interest with little, if any, human assistance. In addition, U.S. patent application Ser. No. 12/892,532, filed Sep. 28, 2010, entitled “Detecting Energy and Environmental Leaks in Indoor Environments Using a Mobile Robot,” incorporated by reference herein, discloses techniques for energy and environmental leak detection in an indoor environment using one or more mobile robots.
While the use of robots has greatly improved the ability to automatically monitor indoor environments, they suffer from a number of limitations, which if overcome, could further extend the utility and efficiency of robots that are monitoring an indoor environment. For example, it is challenging for a plurality of robots to efficiently navigate an indoor environment without getting in each other's way, especially towards the end of the exploration. A number of existing navigation techniques employ the well-known Frontier-Based A* incremental navigation method, first described for a single robot in Peter Hart et al., “A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” SIGART Newsletter, 37: 28-29 (1972), and more recently described in the context of multiple robots, by Yamauchi, “Frontier-Based Exploration Using Multiple Robots,” Proc. of the Int'l Conf. on Autonomous Agents (1998). In addition, a number of existing navigation techniques have also integrated the idea of each robot carrying a “potential field” so that robots are forced to stay at some manually tuned distance from one another. See, e.g., Yong K. Hwang and Narandra Ahuja, “A Potential Field Approach to Path Planning,” IEEE Trans. On Robotics and Automation Actions, Vol. 8, Issue 1 (IEEE, 1992).
A need remains for more efficient navigation methods for robots that automatically navigate, map and monitor environments, particularly well-structured indoor environments such as data centers.